AlphaIgnis

Senior AI/ML Engineer (F/M/D)

🇩🇪 Munich, Alemania Presencial Tecnología Jornada completa Senior Publicado May 28, 2026
Ubicación Munich, Alemania
Modalidad Presencial
Contrato Jornada completa
Seniority Senior
Categoría Tecnología
Categoría IT Data Science y ML
Idioma English
Publicado 28 de mayo de 2026
Última verificación 28 de mayo de 2026
Contexto de JobGrid

Resumen del puesto por JobGrid

Senior AI/ML Engineer (F/M/D) at AlphaIgnis: Munich, Alemania; Presencial; Jornada completa; Senior; Tecnología. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.

  • Location and workplace: Munich, Alemania, Presencial
  • Role classification: Tecnología, Data Science y ML, Jornada completa, Senior
  • Source freshness: checked by JobGrid on 2026-05-28.
  • Application path: candidates continue to the employer application page with non-personal referral tags.

The Opportunity

As a Senior AI/ML Engineer, you will work at the intersection of foundation models and robotics, developing systems that combine vision, language, and action for embodied intelligence.

You will help bridge cutting-edge AI research with real-world robotic applications, ensuring that large-scale models can operate reliably in physical environments. This role sits at the core of building robots that can perceive, reason, and act intelligently in dynamic settings.

Your Responsibilities

  • Design and train foundation models that integrate vision, language, and actions for embodied intelligence
  • Adapt LLMs and VLMs for robotic control, planning, and interactive behavior, enabling context-aware decision making
  • Develop AI-driven control policies for manipulation, grasping, and motion planning using reinforcement learning, imitation learning, and foundation model approaches
  • Build modular, scalable, and high-performance data processing, training, and inference pipelines for large-scale datasets
  • Design reproducible workflows for training, evaluation, and deployment, including benchmarking for generalization, safety, and task success
  • Stay current with advances in AI and robotics, translating research into production systems and contributing to papers, patents, or open-source work

Technologies

  • Python, C++
  • PyTorch (or TensorFlow / JAX)
  • Transformer-based models (LLMs, VLMs, multimodal architectures)
  • Distributed training frameworks
  • Linux, Docker (Kubernetes nice to have)
  • ML pipelines and model deployment systems